| snippet:
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Tree canopy raster dataset was developed for land cover mapping in support with the City of San Diego. The goal of this project is to increase municipal decision makers' understanding of their green infrastructure with the amount of tree canopy that currently exists and the amount of land that could theoretically support tree canopy. It is important to note that this data was mapped to the extent of the 2014 LiDAR dataset recieved from the City of San Diego (generally coastal areas). |
| summary:
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Tree canopy raster dataset was developed for land cover mapping in support with the City of San Diego. The goal of this project is to increase municipal decision makers' understanding of their green infrastructure with the amount of tree canopy that currently exists and the amount of land that could theoretically support tree canopy. It is important to note that this data was mapped to the extent of the 2014 LiDAR dataset recieved from the City of San Diego (generally coastal areas). |
| extent:
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[[-117.600501546662,32.488076263268],[-116.8067108957,33.5109623806843]] |
| accessInformation:
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University of Vermont Spatial Analysis Laboratory in collaboration with City of San Diego. |
| thumbnail:
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thumbnail/thumbnail.png |
| maxScale:
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1.7976931348623157E308 |
| typeKeywords:
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["ArcGIS","ArcGIS Server","Data","Feature Access","Feature Service","providerSDS","Service"] |
| description:
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Tree canopy was derived from 2014 high-resolution remotely sensed data. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. |
| licenseInfo:
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|
| catalogPath:
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|
| title:
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Tree_Canopy_2014_SD |
| type:
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Feature Service |
| url:
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|
| tags:
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["Tree Canopy","Carbon Sequestration","Climate Action Plan","Urban Forestry Master Plan","Land Cover"] |
| culture:
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en-US |
| name:
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Tree_Canopy_2014_SD |
| guid:
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AD8F25C8-C9D8-46AA-86E9-60F83CF58C8B |
| minScale:
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0 |
| spatialReference:
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NAD_1983_StatePlane_California_VI_FIPS_0406_Feet |